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Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning
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Author(s): Adem Assfaw Mekonnen (Addis Ababa Science and Technology University, Ethiopia), Hussien Worku Seid (Addis Ababa Science and Technology University, Ethiopia), Sudhir Kumar Mohapatra (Addis Ababa Science and Technology University, Ethiopia)and Srinivas Prasad (GITAM University, India)
Copyright: 2021
Pages: 19
Source title:
Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch007
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
The timely prognosis of brain tumors is gambling a great role within the pretreatment of patients and keep the life of suffers. The manual classification of brain tumors is a difficult task for radiologists due to the intensity variation pixel information produced by the magnetic resonance machine and it is a very tedious task for a large number of images. A deep learning algorithm becomes a famous algorithm to conquer the problems traditional machine learning algorithms by automatically feature extraction from the input spaces and accurately detect the brain tumors. One of the most important features of deep learning is transferred a gain knowledge strategy to use small datasets. Transfer learning is explored by freezing layers and fine-tuning a pre-trained model to a recommended convolutional neural net model. The proposed model is trained using 4000 real magnetic resonance images datasets. The mean accuracy of the proposed model is found to be 98% for brain tumor classifications with mini-batch size 32 and a learning rate of 0.001.
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